R: An S4 Class to represent a fitted TERGM by MCMC-MLE

mtergm-class {btergm}

R Documentation

An S4 Class to represent a fitted TERGM by MCMC-MLE

Description

An S4 class to represent a fitted TERGM by MCMC-MLE.

Usage

## S4 method for signature 'mtergm'
show(object)

## S4 method for signature 'mtergm'
coef(object, invlogit = FALSE, ...)

## S4 method for signature 'mtergm'
nobs(object)

timesteps.mtergm(object)

## S4 method for signature 'mtergm'
summary(object, ...)

Arguments

`object`	An `mtergm` object.
`invlogit`	Apply inverse logit transformation to the estimates and/or confidence intervals? That is, `\frac{1}{1 + \exp(-x)}`, where `x` is the respective value.
`...`	Currently not in use.

Details

mtergm objects result from MCMC-MLE-based estimation of a TERGM via the mtergm function. They contain the coefficients, standard errors, and p-values, among other details.

Functions

show(mtergm): Show the coefficients of an mtergm object.
coef(mtergm): Return the coefficients of an mtergm object.
nobs(mtergm): Return the coefficients of an mtergm object.
timesteps.mtergm(): Return the number of time steps saved in an mtergm object.
summary(mtergm): Return the coefficients of an mtergm object.

Slots

coef: Object of class "numeric". The coefficients.
se: Object of class "numeric". The standard errors.
pval: Object of class "numeric". The p-values.
nobs: Object of class "numeric". Number of observations.
time.steps: Object of class "numeric". Number of time steps.
formula: Object of class "formula". The original model formula (without indices for the time steps).
formula2: The revised formula with the object references after applying the tergmprepare function.
auto.adjust: Object of class "logical". Indicates whether automatic adjustment of dimensions was done before estimation.
offset: Object of class "logical". Indicates whether an offset matrix with structural zeros was used.
directed: Object of class "logical". Are the dependent networks directed?
bipartite: Object of class "logical". Are the dependent networks bipartite?
estimate: Estimate: either MLE or MPLE.
loglik: Log likelihood of the MLE.
aic: Akaike's Information Criterion.
bic: Bayesian Information Criterion.
ergm: The original ergm object as estimated by the ergm function in the ergm package.
nvertices: Number of vertices.
data: The data after processing by the tergmprepare function.

Author(s)

Philip Leifeld